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mini_autograd_test.py
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import torch
from mini_autograd import Value
def test_basic1():
x1 = Value(-4.0, requires_grad=True)
z1 = 2 * x1 + 2 + x1
h1 = z1 * z1
h1.backward()
print(f"h1 data:{h1.data}, x1 grad:{x1.grad}")
x2 = torch.Tensor([-4.0]).double()
x2.requires_grad = True
z2 = 2 * x2 + 2 + x2
h2 = z2 * z2 # d_h2 / d_z2 = 2 * z2 * (d_z2 / d_x2) = 2 * z2 * 3 = 6 * 14 = 84
h2.backward()
print(f"h2 data:{h2.item()}, x2 grad:{x2.grad.item()}")
assert h1.data == h2.item()
assert x1.grad == x2.grad.item()
def test_basic2():
x1 = Value(3.0, requires_grad=True)
y1 = Value(2.0, requires_grad=True)
z1 = x1**2 + x1*y1 + y1 + 1.0
h1 = z1 * z1
h1.backward()
print(f"h1 data:{h1.data}, x1 grad:{x1.grad}, y1 grad:{y1.grad}")
x2 = torch.Tensor([3.0]).double()
y2 = torch.Tensor([2.0]).double()
x2.requires_grad = True
y2.requires_grad = True
z2 = x2**2 + x2*y2 + y2 + 1.0
h2 = z2 * z2
h2.backward()
print(f"h2 data:{h2.item()}, x2 grad:{x2.grad.item()}, y2 grad:{y2.grad.item()}")
assert h2.item() == h1.data
assert x2.grad.item() == x1.grad and y2.grad.item() == y1.grad
def test_basic3():
x1 = Value(3.0, requires_grad=True)
y1 = Value(2.0, requires_grad=True)
z1 = x1**2 - x1*y1 - y1 + 1.0
h1 = 1 - z1 * z1
h1.backward()
print(f"h1 data:{h1.data}, x1 grad:{x1.grad}, y1 grad:{y1.grad}")
x2 = torch.Tensor([3.0]).double()
y2 = torch.Tensor([2.0]).double()
x2.requires_grad = True
y2.requires_grad = True
z2 = x2**2 - x2*y2 - y2 + 1.0
h2 = 1 - z2 * z2
h2.backward()
print(f"h2 data:{h2.item()}, x2 grad:{x2.grad.item()}, y2 grad:{y2.grad.item()}")
assert h2.item() == h1.data
assert x2.grad.item() == x1.grad and y2.grad.item() == y1.grad
def test_basic4():
"""
a = 2.0
b = a * a
c = 2 * b
d = 3 * b
e = c + d
e
/ \
c d
/ \ / \
2 b 3
|
a*a
"""
a1 = Value(1.5, requires_grad=True)
b1 = a1 * a1
c1 = 2 * b1
d1 = 3 * b1
e1 = c1 + d1
e1.backward()
print(f"e1 data:{e1.data}, a1 grad:{a1.grad}")
a2 = torch.Tensor([1.5]).double()
a2.requires_grad = True
b2 = a2 * a2
c2 = 2 * b2
d2 = 3 * b2
e2 = c2 + d2
e2.backward()
print(f"e2 data:{e2.item()}, a2 grad:{a2.grad.item()}")
assert a1.grad == a2.grad.item() and e1.data == e2.item()
def test_basic5():
"""
a = 2.0
b = a * a
c = 2 / b
d = b / 3
e = c + d
e
/ \
c d
/ \ / \
2 b 3
|
a*a
"""
a1 = Value(1.5, requires_grad=True)
b1 = a1 * a1
c1 = 2 / b1
d1 = b1 / 3
e1 = c1 + d1
e1.backward()
print(f"e1 data:{e1.data}, a1 grad:{a1.grad}")
a2 = torch.Tensor([1.5]).double()
a2.requires_grad = True
b2 = a2 * a2
c2 = 2 / b2
d2 = b2 / 3
e2 = c2 + d2
e2.backward()
print(f"e2 data:{e2.item()}, a2 grad:{a2.grad.item()}")
assert a1.grad == a2.grad.item() and e1.data == e2.item()
def test_basic6():
a = Value(-4.0, requires_grad=True)
b = Value(2.0, requires_grad=True)
c = a + b
d = a * b + b**3
c += c + 1
c += 1 + c + (-a)
d += d * 2 + (b + a).relu()
d += 3 * d + (b - a).relu()
e = c - d
f = e**2
g = f / 2.0
g += 10.0 / f
g.backward()
ama, bma, gma = a, b, g
print("----")
print(a)
print(b)
print(g)
a = torch.Tensor([-4.0]).double()
b = torch.Tensor([2.0]).double()
a.requires_grad = True
b.requires_grad = True
c = a + b
d = a * b + b**3
c = c + c + 1
c = c + 1 + c + (-a)
d = d + d * 2 + (b + a).relu()
d = d + 3 * d + (b - a).relu()
e = c - d
f = e**2
g = f / 2.0
g = g + 10.0 / f
g.backward()
apt, bpt, gpt = a, b, g
tol = 1e-6
# forward pass went well
print(f"gmg.data: {gma.data}, gpt.data.item(): {gpt.data.item()}")
assert abs(gma.data - gpt.data.item()) < tol
# backward pass went well
print(f"amg.grad {ama.grad}, apt.grad.item() {apt.grad.item()}")
assert abs(ama.grad - apt.grad.item()) < tol
assert abs(bma.grad - bpt.grad.item()) < tol
def test_basic7():
x = Value(-4.0, requires_grad=True)
z = 2 * x + 2 + x
q = z.relu() + z * x
h = (z * z).relu()
y = h + q + q * x
y.backward()
xma, yma = x, y
x = torch.Tensor([-4.0]).double()
x.requires_grad = True
z = 2 * x + 2 + x
q = z.relu() + z * x
h = (z * z).relu()
y = h + q + q * x
y.backward()
xpt, ypt = x, y
# forward pass went well
assert yma.data == ypt.data.item()
# backward pass went well
assert xma.grad == xpt.grad.item()
def test_basic8():
x = Value(2.0, requires_grad=True)
y = Value(3.0, requires_grad=True)
z = x**y + y**x + 2
z.backward()
xma, yma, zma = x, y, z
x = torch.Tensor([2.0]).double()
y = torch.Tensor([3.0]).double()
x.requires_grad = True
y.requires_grad = True
z = x**y + y**x + 2
z.backward()
xpt, ypt, zpt = x, y, z
# forward pass went well
assert zma.data == zpt.data.item()
# backward pass went well
assert xma.grad == xpt.grad.item()
assert yma.grad == ypt.grad.item()
def test_basic9():
x = Value(0.2, requires_grad=True)
y = Value(0.8, requires_grad=True)
z = x**y + y**x + 2
z = z.sigmoid()
z.backward()
xma, yma, zma = x, y, z
x = torch.Tensor([0.2]).double()
y = torch.Tensor([0.8]).double()
x.requires_grad = True
y.requires_grad = True
z = x**y + y**x + 2
z = z.sigmoid()
z.backward()
xpt, ypt, zpt = x, y, z
tol = 1e-6
# forward pass went well
assert abs(zma.data - zpt.data.item()) < tol
# backward pass went well
assert abs(xma.grad - xpt.grad.item()) < tol
assert abs(yma.grad - ypt.grad.item()) < tol
def test_basic10():
x = Value(0.2, requires_grad=True)
y = Value(0.8, requires_grad=True)
z = x.log() + y.log()
z = z.sigmoid()
z.backward()
xma, yma, zma = x, y, z
print(x, y, z)
x = torch.Tensor([0.2]).double()
y = torch.Tensor([0.8]).double()
x.requires_grad = True
y.requires_grad = True
z = torch.log(x) + torch.log(y)
z = z.sigmoid()
z.backward()
xpt, ypt, zpt = x, y, z
print(x, y, z)
tol = 1e-6
# forward pass went well
assert abs(zma.data - zpt.data.item()) < tol
# backward pass went well
assert abs(xma.grad - xpt.grad.item()) < tol
assert abs(yma.grad - ypt.grad.item()) < tol
if __name__ == '__main__':
test_basic1()
test_basic2()
test_basic3()
test_basic4()
test_basic5()
test_basic6()
test_basic7()
test_basic8()
test_basic9()
test_basic10()